北京生物医学工程

远程健康监护系统监护信息预报方法

Predicting method of monitoring information in telemonitoring system

作者：蒋贤海谢存禧

单位：广东水利电力职业技术学院(广州501635)

分类号：

出版年·卷·期（页码）：2013·32·4（387-391）

摘要：

目的针对远程健康监护系统中系统预报未来的监护信息数值问题，提出一种基于粒子群优化算法(particle swarm optimization，PSO)的监护信息预报方法。方法采用PSO算法确定预报模型参数，应用最小二乘支持向量机(least square support vector machine，LS-SVM)对未来监护信息数值进行预报。文中构建了监护信息的LS-SVM预报模型，给出了模型参数确定方法。最后，选择PhysioNet标准数据库中的数据，应用该方法进行了实验。结果实验结果表明该监护信息预报方法是有效的。结论基于PSO算法的远程健康监护系统可较好地预测未来的监护信息值，并能较准确地预报监护对象的健康状态。

Objective According to the problem of predicting future monitoring information in tele-health monitoring system，the predicting method of monitoring information is proposed based on particle swarm optimization (PSO). Methods The parameters of predicting model are given based on PSO in this method，and the least square support vector machine (LS-SVM) is adopted to predict monitoring information. Then，the prediction model of monitoring information based on LS-SVM is constructed，and the method of confirming parameters in the model is proposed. Finally，an experiment is carried out with PhysioNet database. Results The experimental results verify the effectiveness of this predicting method for monitoring information. Conclusions A new method based on PSO can be used to predict future monitoring information in tele-health monitoring system，and the health state of the monitored object can be forecasted accurately.

参考文献：

［1］韩秀清,李浩敏,杜芳芳,等. 基于Internet的生理多参数远程实时监护系统设计［J］. 医疗卫生装备,2007,28（4）：3-5.

Han Xiuqing，Li Haomin，Du Fangfang，et al. Design of real-time tele-monitoring system for physiological multi-parameter based on Internet［J］. Medical equipment，2007,28(4):3-5.

［2］Vapnik VN. An overview of statistical learning theory［J］. IEEE Trans Neural Network，1999,10(5):988-999.

［3］Xu K，Xie M，et al. Application of Neural Networks in forecasting Engine Systems Reliability［J］.Applied Soft Computing, 2003,2:255-268.

［4］Hanbay D. An expert system based on least square support vector machines for diagnosis of the valvular heart disease［J］.Expert Systems with Applications，2009,36(3):4232-4238

［5］邹焱飚.谢存禧.基于家庭的远程健康监护系统进展［J］.计算机工程与应用,2005,(10):30-34.

Zou Yanbiao，Xie Cunxi. Development in home health telemonitoring system［J］. Computer engineering and Applications，2005,(10):30-34.

［6］Polat K，Gines S. Breast cancer diagnosis using least square support vector machine［J］. Digital Signal Processing，2007，17:694-701.

［7］邵信光，杨慧中，陈刚.基于粒子群优化算法的支持向量机参数选择及其应用［J］. 控制理论与应用，2006，23（5）:740-743.

Shao Xinguang，Yang Huizhong，Chen Gang. The selection and application of support vector machine based on particle swarm optimization algorithm［J］. Control theory and Applications，2006,23（5）:740-743.

［8］Moody GB，Mark RG，Goldberger AL. PhysioNet:a web-based resource for the study of physiologic signals［J］. IEEE Engineering in Medicine and Biology Society，2001,20(3):70-75.

服务与反馈：

【文章下载】【加入收藏】

提示：您还未登录，请登录！点此登录